Internet-of-Things(IoT) is the computing paradigm converged with different technologies, where diverse devices are connected via the wireless network, acquire environmental information from their equipped sensors, and actuated. IoT applications typically provide smart services to users by interacting with multiple devices connected to the network and are designed by integrating multiple technologies such as sensor network, communication technologies, and software engineering. Moreover, since the concept of IoT has been introduced recently, most of the researches are in the beginning step, which is too early to be practically applied. Due to these facts, developing IoT application results in unconventional technical challenges which have not been observed in typical software applications. And, it is not straightforward to apply conventional project guidelines to IoT application development projects. Hence, there can be many difficulties to successfully complete the projects. Therefore, for successful completion of the projects, we analyze technical challenges occurring in all phases of the project lifecycle, i.e. project preparation stage and development stage. And, we propose the effective solutions to overcome the issues. To verify identified issues and presented solutions, we present the result of applying the solutions to an IoT application development. Through the case study, we evaluate how reasonable the unconventional technical issues are generated and analyze effectiveness of applying the solutions to the application.

Recently, self-adaptive system researches have been done to solve the problems occurred in the dynamic environment. Designing requirement in the self-adaptive system is necessary to recognize and solve the problem for the system, and if a developer reuses existing adaptation strategy to design the requirement, the designing time and cost would be reduced. Therefore, this paper proposes the system which extracts reusable adaptation strategy from the existing self-adaptive system. For the proposal, this paper conceptualizes the self-adaptation elements, defines the adaptation strategy ontology and target system ontology, and presents the process of extracting reusable strategy. This paper also implements proposed system and evaluates the reuse rate of the extracted strategy. As a result, the adaptation strategies extracted by proposed system are exactly operated, and the extraction method of proposed system shows higher reuse rate than a previous method.

A given product`s online product reviews build up to form largely positive or negative reviews or mixed reviews that include both the positive and negative reviews. While the homogeneously positive or negative reviews help readers identify the generally praised or criticized product, the mixed reviews with minority opinions potentially contain valuable information about the product. We present a method of retrieving minority opinions from the online product reviews using the skewness of positive/negative reviews. The proposed method first classifies the positive/negative product reviews using a sentiment dictionary and then calculates the skewness of the classified results to identify minority reviews. Minority review retrieval experiments were conducted on smartphone and movie reviews, and the F1-measures were 24.6% (smartphone) and 15.9% (movie) and the accuracies were 56.8% and 46.8% when the individual reviews` sentiment classification accuracies were 85.3% and 78.8%. The theoretical performance of minority review retrieval is also discussed.

Since automatic social engineering based spam attacks induce for users to click or receive the short message service (SMS), e-mail, site address and make a relationship with an unknown friend, it is very easy for them to active in online social networks. The previous spam detection schemes only apply manual filtering of the system managers or labeling classifications regardless of the features of social networks. In this paper, we propose the spam detection metric after reflecting on a couple of features of social networks followed by analysis of real social network data set, Twitter spam. In addition, we provide the online social networks based unsupervised scheme for automated social engineering spam with self organizing map (SOM). Through the performance evaluation, we show the detection accuracy up to 90% and the possibility of real time training for the spam detection without the manager.

As the importance of software to society has grown, more and more schools worldwide teach coding basics in the classroom. Despite the rapid spread of coding instruction in grade schools, experience in the classroom is certainly limited because there is a gap between the curriculum and the existing computing environment such as the mobile and cloud computing. We propose an approach to fill this gap by using a mobile environment and the robot on the cloud-based platform for effective teaching. In this paper, we propose an architecture called Cloudboard that enables knowledge sharing and collaboration among knowledge providers in the cloud-based robot platforms. We also describe five representative architectural patterns that are referenced and analyzed to design the Cloudboard architecture. Our early experimental results show that the Cloudboard can be effective in the development of collective robotic systems.

As smart devices get popular, research on gesture recognition using their embedded-accelerometer draw attention. As Dynamic Time Warping(DTW), recently, has been used to perform gesture recognition on data sequence from accelerometer, in this paper we propose Feature-Strengthened Gesture Recognition(FsGr) Model which can improve the recognition success rate when DTW is used. FsGr model defines feature-strengthened parts of data sequences to similar gestures which might produce unsuccessful recognition, and performs additional DTW on them to improve the recognition rate. In training phase, FsGr model identifies sets of similar gestures, and analyze features of gestures per each set. During recognition phase, it makes additional recognition attempt based on the result of feature analysis to improve the recognition success rate, when the result of first recognition attempt belongs to a set of similar gestures. We present the performance result of FsGr model, by experimenting the recognition of lower case alphabets.